Hessian Affine + SIFT keypoints in Python
This is an implementation of Hessian-Affine detector.
The implementation uses a Lowe's (Lowe 1999, Lowe 2004) like pyramid to sample Gaussian scale-space and localizes local extrema of the Detetminant of Hessian Matrix operator computed on normalized derivatives. Then a Baumberg-Lindeberg discovery of a local affine shape is employed (Lindeberg 1998, Baumberg 2000, Mikolajzyk 2002) to compute affine shape of each det of Hessian extrema. Finally a local neighbourhood is normalized to a fixed size patch and SIFT descriptor(Lowe 1999, Lowe 2004) computed.
There are wheels publishe on pypi using cibuildwheel.
Implementation depends on OpenCV (2.3.1+). Although, the code is original, the affine iteration and normalization was derived from the code of Krystian Mikolajczyk.
The SIFT descriptor code was patented under a US Patent 6,711,293, which expired on March 7th 2019, so the license is no longer required for use.
NOTE THIS IS NO LONGER THE CASE. WE MAY REINSTATE THIS.
The built binary rewrites output file: <input_image_name>.hesaff.sift
The output format is compatible with the binaries available from the page "Affine Covariant Features". The geometry of an affine region is specified by: u,v,a,b,c in a(x-u)(x-u)+2b(x-u)(y-v)+c(y-v)(y-v)=1. The top left corner of the image is at (u,v)=(0,0). The geometry of an affine region is followed by N descriptor values (N = 128).
N m u1 v1 a1 b1 c1 d1(1) d1(2) d1(3) ... d1(N) : : um vm am bm cm dm(1) dm(2) dm(3) ... dm(N)
If you use this code, please refer to
Perdoch, M. and Chum, O. and Matas, J.: Efficient Representation of Local Geometry for Large Scale Object Retrieval. In proceedings of CVPR09. June 2009.
TBD: A reference to technical report describing the details and some retrieval results will be placed here.
Requires opencv. On ubuntu you can:
sudo apt-get install libopencv-dev. You can also build / use wheels.